802 Current Pharmaceutical Design, 2010, 16, 802-813 Systems Biology and Longevity: An Emerging Approach to Identify Innovative Anti- Aging Targets and Strategies E. Cevenini1,2, E. Bellavista1,2, P. Tieri1,2, G. Castellani1,3, F. Lescai1,2, M. Francesconi1,3, M. Mishto1,2,4, A. Santoro1,2, S. Valensin1,2, S. Salvioli1,2, M. Capri1,2, A. Zaikin5, D. Monti6, J. P. de Magalhães7 and C. Franceschi1,2,* 1C.I.G. - Interdepartmental Centre “L. Galvani”, University of Bologna, Bologna, Italy; 2Department of Experimental Pathology, University of Bologna, Bologna, Italy; 3Department of Physics, University of Bologna, Bologna, Italy; 4Institute of Biochemistry, Medical Faculty Charité, Berlin, Germany; 5Departments of Mathematics and Institute of Women Health, University College London, London, United Kingdom; 6Department of Experimental Pathology and Oncology, University of Florence, Florence, Italy; 7School of Biological Sciences, University of Liverpool, Liverpool, United Kingdom Abstract: Human aging and longevity are complex and multi-factorial traits that result from a combination of environmental, genetic, epigenetic and stochastic factors, each contributing to the overall phenotype. The multi-factorial process of aging acts at different levels of complexity, from molecule to cell, from organ to organ systems and finally to organism, giving rise to the dynamic “aging mosaic”. At present, an increasing amount of experimental data on genetics, genomics, proteomics and other –omics are available thanks to new high- throughput technologies but a comprehensive model for the study of human aging and longevity is still lacking. Systems biology represents a strategy to integrate and quantify the existing knowledge from different sources into predictive models, to be later tested and then implemented with new experimental data for validation and refinement in a recursive process. The ultimate goal is to compact the new acquired knowledge into a single picture, ideally able to characterize the phenotype at systemic/organism level. In this review we will briefly discuss the aging phenotype in a systems biology perspective, showing four specific examples at different levels of complexity, from a systemic process (inflammation) to a cascade-process pathways (coagulation) and from cellular organelle (proteasome) to single gene-network (PON-1), which could also represent targets for anti-aging strategies. Keywords: Aging mosaic, longevity, systems biology, inflammaging, coagulation, proteasome, PON-1. 1. AGING AND LONGEVITY To this regard, it has been reported that the identification of genetic variants underlying complex traits and diseases (such as 1.1. Aging as a Complex Trait psychopathology, hypertension, cardiovascular diseases [5] and The study on human aging and longevity has become a very hot neurodegenerative diseases [6]) may take advantage from the study topic in the last years because of the so-called revolution in of gene expression in particular environmental conditions [5]. The demography, which led to the remarkable increase in the number of studies on human aging and longevity are further complicated by people over the age of 65 or 80 years living in Western countries the fact that human populations are heterogeneous from the point of but also in some emerging countries such as China and India [1]. view of genetic pool, life style, cultural habits, education, economic The data collected during the last 20 years in different models status and social network. All these components are different from suggest that the picture of the aging phenotype is fragmented and population to population, and each population is characterised by a above all qualitative and that we are far from an exhaustive and unique combination of them. This fact renders the studies difficult quantitative scenario. This incomplete knowledge comes out from to compare and the results very often discordant. Finally, all these several bias: 1. few studies evaluate many parameters at the same considerations also apply to gender difference, since gender appears time in the same individual; 2. the collected data are not of high- to be a crucial player in the cross-talk between genes, environment dimensionality; 3. longitudinal studies, which are the most infor- and health [7]. The development of effective and realistic strategies mative, are scanty. Another factor contributing to the complexity of for aging intervention, prevention and therapies may be facilitated the problem is that human aging and longevity are complex and by this integrated and multi-faced view. Indeed, it has been multi-factorial traits, generally considered as the result of the proposed that the manipulation of both genes and environment at combination of environment, genetics (and epigenetics) and the same time can open up novel possibilities of aging intervention stochasticity, each making variable contributions to the overall and prevention [8]. phenotype [2-4]. It is hypothesised that the importance of each component changes with the passing of time, as shown in Fig. (1). 1.2. Aging as a Mosaic In this cartoon the relative importance of each component as well as It is conceivable that longevity could be achieved by different the level of interaction with the other components are represented strategies and by different combinations of genetics, epigenetics, as colour tones and overlapping of circles. According to this environment and stochasticity and the result, i.e. the aging hypothesis, genetics is less important in the early adult phase of life phenotype, is very heterogeneous. Moreover, it is emerging that the and becomes more important in old age. Moreover, these different multi-factorial process of aging does not occur only at organism components interact with each other, in particular genetics and level, but it also acts differently in each organ system, organ, tissue environment. and even in each single cell of the body, determining a different aging rate for each of them. In mice it has been observed that different tissues age in a coordinated fashion [9], while in humans this is still to be ascertained. As a consequence of these different *Address correspondence to this author at the Department of Experimental aging rates, the aged body could be considered as a mosaic of Pathology, University of Bologna, Via San Giacomo 12, 40126 Bologna, tissues and organs displaying a different level of senescence, a Italy; Tel: +39 051 2094772; Fax: +39 051 2094773; situation we proposed to indicate as “aging mosaic” [10], as E-mail: [email protected] 1381-6128/10 $55.00+.00 © 2010 Bentham Science Publishers Ltd. Systems Biology and Longevity Current Pharmaceutical Design, 2010, Vol. 16, No. 7 803 Fig. (1). The changes on relative importance and interaction between the main determinants of healthy aging and longevity. The colour tones are a function of the relative importance of each component (E=environment, G=genetics and S=stochasticity). The increase intensity of the colour (e.g. from yellow to red) means increased importance. The overlapping of the circles is a function of the interactions between these components. The in utero environment has been highlighted in cyan as it is profoundly different from the post natal one, highlighted in green tones. exemplified in Fig. (2). In this figure, we represented the human that low-grade stressors can be assimilated to what is referred to as body as a mosaic of 12 organ systems (indicated as rectangles) functional or constructive noise. The role of noise in biological according to Hunter and Borg [11], each of them displaying a systems is under investigation, and, for example, it is now evident different level of senescence (represented by black dots). that the stochastic or inherently random nature of the biochemical reactions of gene expression may contribute to the phenotypic Thus, even in the same individual, the aging process appears to follow different trajectories in different organs, tissues and cells, variability in individuals [15]. Thus, noise, which was often considered “unimportant” by traditional statistical methods and which are variably affected and accumulate unrepaired damages at models, should be taken into account during data analysis, since it different rates. An example, among many others, is the brain where different regions, such as cortex, hippocampus and cerebellum, may hide un-analyzed information [16]. For example, in gene expression analysis, only genes whose expression profile differs show different levels of neurodegeneration and inflammation in the from an established threshold were evaluated and the others were same subject [12,13]. In mice, a great heterogeneity in the amount of transcriptional changes with age in different tissues was found classified as “noise” and erroneously excluded [15]. It is also important to note that noise-induced phenomena have been [9]. Based on the pattern of age-related transcriptional changes, experimentally detected in many levels of biological functionality, three aging patterns have been proposed: (1) a pattern common to neural tissues, (2) a pattern for vascular tissues, and (3) a pattern for i.e. in plankton detection by paddle fish [17], in the retrieval processes of the human memory [18], and in human brain waves steroid-responsive tissues [9]. [19]. How can noise potentially play a constructive role in the Moreover, this mosaic is dynamic, meaning that it changes with system dynamics of main components and compartments of a time, owing to the complex non linear interactions among the biological organism? As regards the immunoproteasome [20], a key different components, resulting in a final phenotype that is not cellular organelle that will be discussed later in this review, it has easily predictable by the study of its single sub-components. been addressed whether the protein translocation inside the Changes in this mosaic derive from different forms of stressors to proteasome chamber can be driven by fluctuation, and a toy-model which the body is exposed lifelong. The adaptive response to these has been derived to show the probable mechanism of translocation perturbations can follow two trajectories: 1. in case of exposure to a [21]. Even if at the moment there is no experimental verification of repeated low grade stimulus, the mosaic is able to positively the proposed hypothesis, however, it can be expected that noise- remodel the mechanisms of maintenance which are up-regulated induced behaviour could play a major role in the immunopro- and the systemic result is the resistance to stress (hormesis); 2. teasome functioning, making this aspect worth of deeper conversely, in case of exposure to a stronger stimulus (over a investigation. specific threshold) in terms of intensity and persistence, the remodelling process still occurs, but the mechanisms of main- Currently, most of the studies focused on human aging refer to a single district of the mosaic (generally the most easily available tenance are less efficient and the global result is negative and detrimental for health and survival [14]. Therefore, a strategy to tissues). For example, experimental results indicate that in vivo cell increase healthy aging and longevity could be to favour the types from skin of aged individuals are likely to compose a mosaic made of cells irreversibly growth arrested (replicative senescence) hormetic response by transforming intense and persistent stressors into low-grade ones. In this perspective it is also interesting to note and cells maintaining a young proliferative phenotype. 804 Current Pharmaceutical Design, 2010, Vol. 16, No. 7 Cevenini et al. Fig. (2). Aging as a dynamic mosaic. The body (young or aged) is represented as a square subdivided in 12 rectangles, each of them representing an organ system. The amount of dots indicates the rate of aging affecting each system. To this regard, ex vivo studies performed on human diploid middle-aged and extreme long-living individuals. We named this fibroblasts (HDFs) from young and centenarian subjects show phenomenon Complex Allele Timing [3]. The first example of interesting results: they are characterized by the same proliferative such kind of trajectories was reported since 1998 on APOB [30] capacity and the same telomere length [22], but they differ for the and the latest one is represented by Klotho KL-VS polymorphism, gene expression profile [23]. Moreover, in vitro studies show for for which a significant increase of the heterozygous Klotho example that the higher level of stress to which these cells have genotype in the class of elderly people compared to young controls been exposed throughout their life span, the higher proportion of emerged, while no difference was present between centenarians and the cells of this mosaic will undergo stress-induced premature young controls. Thus, this KL-VS heterozygous genotype is favour- senescence (SIPS) rather than telomere-shortening dependent able for survival in old people, its beneficial effect decreasing senescence. All cell types undergoing SIPS in vivo, most notably thereafter, and becoming no more evident at the extreme ages. Such those in stressful conditions, are likely to participate in the tissutal a non monotonic age-related trajectory of the Klotho KL-VS changes observed along aging. For instance, HDFs exposed in vivo genotype frequency is compatible with the hypothesis that alleles and in vitro to pro-inflammatory cytokines display biomarkers of and genotypes involved in aging and longevity may exert their senescence and might participate in the degradation of the extra- biological effect at specific time windows [31]. It is noteworthy that cellular matrix observed in aging [24]. In addition, despite support this conceptualization is supported by the use of demographic for the idea that senescence is a beneficial anticancer mechanism information, that, together with data on genetic markers, allowed us (i.e. expression of anti-proliferative genes), it is emerging that the to calculate hazard rates, relative risks, and survival functions for secretome of senescent cells display an inflammatory phenotype candidate genes or genotypes, providing a powerful tool for that can also be deleterious and might contribute to age-related analyzing their influence on longevity and survival [32]. This pathologies [25]. modelling approach allowed us to predict trajectories in genetic variation frequencies that have been subsequently confirmed by A more comprehensive approach to understand the complexity experimental data [32,33]. of this mosaic would be the simultaneous study of several organs at once; at present, this strategy has been applied only to animal Moreover, the role of epigenetics (DNA methylation, histone models, as reported in mice by Schumacher B and co-workers [26]. post-transcriptional modifications, including methylation, acety- However it should be taken into account that extrapolation of lation, ubiquitination and phosphorylation) in the aging process results from experimental animals (mice) to humans is not always should also be considered within this scenario because it includes straightforward, mainly due to the quantitative differences in regulatory mechanisms that could play a pivotal role in cellular maintenance and reproduction [27]. homeostasis, age-related diseases, such as human cancer, as well as lifespan. To this regard, "aging epigenetics" became an emerging Within the complex scenario of such an interaction of different tissues and organs, each of them having intrinsic regulatory mecha- discipline [34,35]. The epigenetic alterations that accumulate with nisms as well as different aging rates, we proposed to go beyond the age, such as the global DNA hypomethylation (above all on repeated sequences), the promoter hyper methylation of Werner simplistic assumption of longevity genes playing the same role all along human life. The interpretation of antagonistic pleiotropy Syndrome genes and lamin genes, aberrant CpG island promoter [28,29] paved the way for a wider interpretation of genes (and their hypermethylation, DNA methyltransferase (DNMT) overexpression and histone modifications, could be responsible, at least in part, for polymorphisms) functioning: the reality is even more complex than antagonistic pleiotropy is capable to describe. Several experimental the phenotype of aged cells, tissues and organs, being in turn evidences suggest that the role of genes on physiology and responsible for age-related changes in gene expression [4,36]. However, no data are available on the epigenetic of human pathology can change at different phases of human life. In genetics, these evidences are represented by complex trajectories in the (extreme) longevity and the global DNA methylation and the frequencies of functional polymorphisms in population cohorts of methylation status of specific candidate loci for longevity is at present lacking, as well as epigenetics applied to population gene- different ages. The presence of such trajectories suggests that the same polymorphism can have different biological effects in young, tics studies. Systems Biology and Longevity Current Pharmaceutical Design, 2010, Vol. 16, No. 7 805 In this complex scenario, where a unique model for the study of In spite of this complex scenario, immunological studies and human aging and longevity does not exist, a systems biology studies on centenarians revealed that common mechanisms occur- approach that combines and quantifies genetics, genomics, proteo- red both in very different age-related diseases, such as diabetes and mics and other –omics fields is necessary to handle the ever neurodegeneration, and in healthy long-living subjects. One is the increasing amount of experimental data made available by new persistent, low grade inflammatory process which develops with high-throughput technologies. The final aim we are pursuing is to advancing age and that we called “inflammaging” [46]. Therefore, use a systems biology perspective to grasp the complexity of anti-inflammatory therapies could efficiently contribute to slow “aging” and “longevity”; in order to have a healthy old age, a down aging and age-associated pathologies, thus increasing the systems perspective of medicine could promote the arrangement of possibility to reach longevity. In a general perspective, the pre- advanced personalized therapies specifically aimed, for example, to vention or the postponement of the aging process could have much target the individual patient's genetic defects [10,37,38]. greater benefit than those targeted at individual disease [47]. In addition, combining the already known empirical methods of anti- 2. METHODOLOGICAL APPROACHES TO AGING AS A aging medicine with unique genetic profile of each human (gene- COMPLEX MOSAIC pass) may render new awarding opportunities for the further advancements of human longevity programs [48]. 2.1. Systems Biology, Aging and Longevity Aging can be studied at several levels of increasing complexity: 2.2. Resources, Databases and Models molecular, organelle, cellular, organ, organ system and organism Post-genome technologies, such as microarrays and RNAi, have level. For a long time the cellular level was used as the most provided researchers with powerful, high-throughput tools to study integrate level to study aging and longevity, generating a huge aging [49]. To collect, manage, and organize the massive amounts amount of data, directly transferred to tissues, organs, organ of data generated and help researchers analyze the data, a number systems and the whole organism. Despite an enormous literature of resources and databases have been assembled. The Human regarding physiological changes during aging in different organs, Aging Genomic Resources are one of such collections of databases only few studies have been performed at organ system and and tools, which are of particular interest to systems biology of organism level and it is emerging that many findings obtained from aging. Databases include GenAge, an accurate database of genes studies at cellular level often are not informative about the scenario associated with aging in model organisms as well as a selection of of higher levels of complexity. Only now scientists are trying to candidate aging-related human genes. By providing an annotated integrate the findings obtained by studies performed at different list of genes associated with aging, GenAge can help researchers levels into a coherent framework. In this new perspective, systems study how genes interact with each and with the environment to biology represents a modern science whose aim is to operate at a collectively modulate aging [50]. systemic level, by using the most integrated models (organ and Microarrays, by measuring gene expression in a high-through- organism). Thus, it moves biology from a traditional microscopic/ put fashion, allow molecular changes during aging to be charac- qualitative perspective to a macroscopic/quantitative one, allowing terized with unprecedented power and several studies using researchers to integrate and quantify the huge amount but microarrays have now been performed. One database of gene fragmented data that nowadays can be obtained by high-throughput expression profiles during aging, neurodegenerative diseases, and in technologies [39]. In addition, it also offers tools to develop interventions affecting aging is the Gene Aging Nexus, which predictive mathematical models and networks [40], able to evaluate features a compilation of microarray data and a data analysis and compare potential explanations for biological data [41]. Thus, platform (http://gan.usc.edu/) [51]. Another gene expression data- systems biology represents a strategy to integrate and quantify the base is AGEMAP, which provides a catalogue of gene expression existing knowledge and data from different sources into models, to changes with age in different mouse tissues [9]. These resources be later tested and then supported with experimental data for open new opportunities to analyze how multiple genes and validation and refinement, in a recursive process of “wet and dry” mechanisms affect aging, how these gene expression changes are experiments, that will be discussed in the last section of this review. regulated, and which genes and mechanisms may represent targets The ultimate goal is to “compact” the new acquired knowledge into for manipulations. a single picture, ideally able to characterize the phenotype at systemic/organism level. One focus of the systems biology approach is to model data mathematically in order to better interpret the processes under High-throughput technologies generate complex and high study, perform computer simulations, and gain novel insights. dimensionality data that need appropriate statistical analysis, such Ultimately, the goal of modelling biological processes is to shift as nonlinear methods [42], and above all a sophisticated inter- from describing phenotypes to predicting their behaviour and how pretative approach in order to get insight on their biological they can be manipulated. In the context of biogerontology, accurate meaning. As an example, visualization techniques, interaction maps in silico models of aging would help identify key components (e.g., and pathway diagrams can be of great value in order to understand proteins) that influence aging which would not only guide how all molecular and cellular components and modules are experiments but provide novel therapeutic targets to counteract the intertwined and work together to determine the basic structures and effects of aging in particular age-related diseases. the functions of the organism's complex machinery. This approach has been used by Raza and colleagues [43] to reconstruct a logically Considering the complexity of aging, however, thus far models represented pathway map, integrating four pathways central to of aging have focused either on specific components of mechanisms macrophage activation (interferon signalling, NF-(cid:1)B, apoptosis, of aging or at higher levels of abstraction. An example of the toll-like receptor pathways), and representing them as one integ- former is a mathematical model of the heat shock system, which rated pathway due to their strongly overlapping interactions. Of has been implicated in aging and age-related diseases [52]. Higher- course these new methods represent a step further, but we are still level models have focused on gene and protein networks . It is far from having powerful tools able to capture the complexity of the possible to build proteomic networks for candidate gene prioriti- problem and to reach the ultimate goal, that is combining and zation. Proteins which interact with proteins associated with aging quantifying the bulk of available data in specific fields, including are then selected as candidates [53]. Data on genes associated with multifactorial diseases, such as neurodegenerative disorders aging and their interactions can also be integrated with microarray [44,45], aging and longevity [38], vaccinology, preventive medi- data to derive candidate genes whose involvement in aging can then cine, proteolityc systems (proteasome). be tested experimentally [54]. Eventually, the goal is to integrate different types of data into increasingly more detailed. Interestingly, 806 Current Pharmaceutical Design, 2010, Vol. 16, No. 7 Cevenini et al. John D. Furber has been assembling a network model of human (T2D), metabolic syndrome (MS), neurodegeneration, sarcopenia aging at various biological levels. The model is being constructed and cancer, among others. based on a literature review from which components of aging are An age-related increase in the production of inflammatory incorporated and their causal relationships described (http:// compounds occurs in the immune system [58,59], brain [60], www.legendarypharma.com/chartbg.html). adipose tissue [61] and muscle [62], but the possible contribution to inflammaging of other organs or districts, such as gut microbiota 3. EXAMPLES and liver, is largely unexplored. Taking into account that the final aim of the systems biology of At present a major unsolved problem in biology and medicine is aging is to characterize the phenotype at systemic/organism level, the source(s) of the inflammatory stimuli underpinning and systems biology can be also applied at multiscale levels on the basis sustaining inflammaging. Moreover, the signaling circuitry and the of the specific questions to be answered. Thus, studies at molecular, cross-talk among the various tissues and organs involved in cellular and organelle levels can be performed and evaluated by inflammaging are far from being clear. Therefore, systems biology systems biology because even a single gene can play a role in the represents the most powerful and comprehensive approach to definition of a complex phenotype, such as aging and longevity, characterize the systemic nature of inflammaging, for example by since it is part of a network where genes and product genes interact. developing predictive models of inflammaging, urgently needed to Any perturbation of a single component can modify the whole set up strategies for the modulation of inflammation, by acting on network. strategic targets with global, systemic effect on the whole process. According to our experience, in this review we propose four A key point in such a complex framework is the dynamics of examples where systems biology can be applied at different levels signaling pathways crucial to inflammation, such as those related to of complexity, from a systemic process (inflammation) to a NF-(cid:1)B transcription factor activation. NF-(cid:1)B regulates genes that cascade-process pathway (coagulation) and from cellular organelle in turn control cell proliferation, cell survival, as well as innate and (proteasome) to single gene-network (PON-1), all considered in an adaptive immune response. In Fig. (4) we provide a partial integrated vision. We do not treat here other complex processes, reconstruction of its network of interactions. The sheer complexity since they are already examined in recent papers, such as neuro- of such crucial signalling system is intuitively evident from the degenerative disorders [44,45] and apoptosis [38]. The rationale is intricate network of interactions among input and output signals, hidden in the different levels of analysis, from integrated to single mediator proteins and feedback loops. Many proteins (in red) effects, which concur to the determination of the aging phenotype. directly involved in such interactome are in turn products of genes 3.1. Inflammation that are controlled and regulated in their expression by NF-(cid:1)B. Framing structure and dynamics of the NF-(cid:1)B interactome in a Inflammation is a systemic physiological process fundamental wider, systemic picture will be a significant step toward a better for survival, which involves a variety of cells, organs and organ understanding of how it globally regulates diverse gene programs systems, as depicted in Fig. (3). and phenotypes. This fact gives rise to the necessity of a systemic, multiscale approach able to take into consideration all the crucial levels of complexity into an unique framework, from protein- protein interactions to gene expression, from cellular to tissue response and finally to the dynamics of organs. 3.2. Coagulation Coagulation is a typically complex, multiscale process involving in a delicate and time-evolving balance a number of cellular and molecular components. Cells (platelets), protein fac- tors, cofactors and regulators actively participating through diffe- rent pathways converge into the coagulation cascade to yield the conversion of fibrinogen to fibrin, the building block of a hae- mostatic plug. The haemostatic process is finally down-regulated by the intersection of the cascade with anticoagulant pathways. Aging is associated with an increase of many proteins of blood coagulation and with an impairment of the fibrinolysis process, an alarming framework that opens the way for thromboembolic and vascular diseases. Centenarian individuals are characterized by a state of hyper- or pro-coagulability, and by the possession of atherothrombotic risk markers and high-risk alleles. Paradoxically, this condition appears to be compatible with longevity and health. Experimental evidence shows that the increased activity of coagulation enzymes in centenarians does not necessarily correlate Fig. (3). Inflammaging as a systemic phenomenon. Many organs and with augmented thrombosis risk [63-66]. Such data on homeostasis organ systems of the body are strictly interconnected and together contribute profiles of long-lived and centenarian individuals highlight once to the increasing low-grade inflammation that occurs with age. again the pressing need for a systemic and multidisciplinary app- roach to complex and multiscale phenomena such as coagulation Despite the well-recognized systemic character of inflam- and, at a higher level, aging. mation, our knowledge of this multi-factorial phenomenon is highly In this perspective, systemic and quantitative modelling fragmented and its comprehensive understanding is still in its strategies have been successfully applied to uncover coagulation infancy [55]. cascade kinetics and platelet homeostasis. Luan and co-workers In the aging field, this consideration is particularly important [67] built a mechanistic model of the human coagulation cascade for “inflammaging”, [46,56,57], which appears to be important in that, despite the uncertainty inbuilt in modelling such a complex the pathogenesis of cardiovascular diseases (CVD), type 2 diabetes and multi-level system, has been able to identify critical points in Systems Biology and Longevity Current Pharmaceutical Design, 2010, Vol. 16, No. 7 807 Fig. (4). A partial reconstruction of the NF-(cid:3)B transcription factors interactome. Proteins involved in the NF-(cid:3)B pathway (data extracted from literature) are grouped on the basis of their main functional category (each category is inscribed in dashed squares). Direct interactions among proteins are shown (solid blue lines). Proteins coloured in red derive from genes whose expression is directly regulated by NF-(cid:3)B transcription factors. the formation of thrombin. Indeed, simulations performed on a generate novel predictions and provide further valuable expla- literature-derived network of 92 protein complexes and 148 kinetic nations, highlighting for example that, in the ADP-dependent interactions yielded platelet activation and thrombin generation platelet activation, thrombin is a more potent agonist than ADP, profiles consistent with experimentally measured parameters. These thanks to differences in receptor copy number. Thus, the power of results have led to determine which processes can be considered as this integrated computational approach relies on the capability to “points of fragility”, i.e. if and to which extent they are sensitive or yield, through recursive refinements and developments, more and not to parameters changes. The model-based screening has thus more accurate predictions of known as well as unknown pheno- qualitatively identified fragile and robust key mechanisms and mena in platelet biology. processes, which can be considered as putative drug targets, especially when concomitantly supported with thrombosis treat- 3.3. Proteasome ment literature and clinical trials data. This represents a clear The proteasome is an additional candidate target to be studied example of the therapeutic potentiality of the systemic approach in in a systems biology perspective. It is a self compartimentalized this field. protease that could be considered an organelle, conserved during Furthermore, as reported by Purvis and colleagues [68], existing evolution, which is involved in the regulation of all the metabolic kinetic information for 70 species, 132 kinetic rate constants and 77 pathway, in the production of MHC I epitopes and in the protein reactions was computationally combined with experimental results quality control, suggesting its centrality in maintaining the cellular and published data derived from different sources. This integrative homeostasis and thus contributing to reach longevity. Even if it acts approach was able to predict key parameters in resting and acti- at the molecular level, its effects are relevant at systemic level. The vated platelets, replicating experimental data at both averaged 26S proteasome consists of a catalytic 20S core (a four-ring platelet and single platelet response. The model was also able to structure with seven different subunits in each ring, arrayed as (cid:1)7(cid:2)7(cid:2)7(cid:1)7) and two 19S regulatory complexes. Alternatively, 808 Current Pharmaceutical Design, 2010, Vol. 16, No. 7 Cevenini et al. proteasome activator PA28-(cid:1)(cid:2) or -(cid:3) can bind to the end of the 20S promotes cellular resistance to oxidants and confers extension of core [20,69]. In cells exposed to pro-inflammatory stimuli such as human fibroblasts lifespan [83]. A similar stimulatory effect has IFN-(cid:3) and TNF-(cid:1), the constitutive (cid:2) subunits with catalytic activity been observed by an algae extract on human keratinocytes, as it ((cid:2)1, (cid:2)2, (cid:2)5) are replaced by homologue subunits ((cid:2)1i or LMP2, (cid:2)2i provides protection from UVA and UVB irradiation [84], and by or MECL-1 and (cid:2)5i or LMP7, respectively) and incorporated into a Tocopherol (Vitamin E) and linoleic acid [85]. newly synthesized alternative proteasome form, the immunoprotea- some. More recently, a variant, named thymoproteasome, has been 3.4. PON-1 described in thymus [70]. Because constitutive and inducible (cid:2) In this final example, the systems biology approach is applied at catalytic subunits often coexist in cells, the presence of intermediate a single gene level, which can have a great impact on several age- type proteasomes with asymmetric subunit composition, different related diseases and longevity as proposed by the conceptualization proteolytic activities and subcellular localization, have been also of the “diseasome” [86]. Human serum Paraoxonase1 (PON1) is reported [71]. Considering the broad range of proteasome substrates considered not only for its role in aging: PON1 is one of the most and the pivotal role of proteasomes in many cellular pathways, studied gene regarding cardiovascular risk, oxidative stress and alteration in the ubiquitin-proteasome system likely leads to the inflammation. PON1 is primarily synthesised by the liver and high dysregulation of homeostasis and the development of multiple density lipoprotein (HDL) is the serum transport vector for this pathologies [72]. An age-related loss of proteasome function has enzyme that is firstly inserted into the external membrane of the been reported in several human tissues (muscle, lens, lymphocytes secreting cell, and then transferred to HDL [87]. The main function and epidermis), in aged tissues of other mammals (mice, rats and of paraoxonase is to inhibit low density lipoprotein (LDL) oxidation bovines), in human primary cultures undergoing senescence [73] as by destroying the biologically active phospholipids in oxidatively well as in different neurodegenerative pathologies (Alzheimer – modified LDL [88,89]. PON1 significantly reduces the ability of AD-, Parkinson –PD-, Huntington –HD- disease and Amyotrophic oxidized LDL to trigger monocyte-endothelial cell interactions Lateral Sclerosis -ALS-) [12,13]. To date, however, several aspects [90]. Also, it hydrolyzes oxidized LDL-associated compounds, of the proteasome biology and functionality (i.e proteasome which stimulate the production of cytokines, interleukin-8 and interaction networks, proteasome composition in different tissues monocyte colony-stimulating factors and induce adhesion of and organs, degradation rate, modulators, among other) are monocytes to the endothelial surface [91,92]. PON1 activity fragmentary and remain elusive. A very recent interaction map of contributes to prevent the formation of atheromatous plaques. There the yeast 26S proteasome [74] and novel algorithm for subnetwork is a growing body of evidence that reduced activity of the HDL- reconstruction in Saccharomyces Cerevisiae [75] have shown the associated enzyme paraoxonase is predictive of vascular disease in involvement of proteasome in pivotal cellular processes. However, humans, including results from prospective studies [93,94]. Low a systems biology approach to study the proteasome in both normal levels of PON1 activity have been found in numerous pathological and pathological conditions, is still in its infancy. A first attempt to conditions, such as cardiovascular disease, type1 and 2 diabetes, describe, in a systems biology framework, the ubiquitin-proteasome hypercholesterolemia, metabolic syndrome and renal failure [95- system in normal homeostasis as well as during aging, has been 99]. All these pathologies share a pro-inflammatory baseline reported by Proctor and co-worker [76], while a more recent model condition and it has now been shown that HDL composition is of on-going conflicts between the aggregation of (cid:1)-synuclein altered during the acute phase response of the innate immune ((cid:1)SN) and the maintenance of a functional proteasome in PD has system. Polymorphisms located in PON1 coding region are due to been described [77]. Concerning the knowledge of proteasome an aminoacid substitution at position 192 (Gln-Arg = Q-R alleles) degradation processes, further insight are provided by an in silico and at position 55 (Leu-Met = L-M alleles). The Q192R poly- mathematical model, focused on in vitro proteasome degradation morphism imparts different catalytic activity toward some organ assay, developed by our group [78]. This model, named phosphorus (OP) substrates. Specifically, the 192R isoform ProteaMAlg (Proteasome Modeling Algorithm), is an extension of a hydrolyzes more efficiently paraoxon, however the 192Q isoform is previous one [79] and it describes the kinetics of specific protein more efficient in hydrolyzing diazoxon, soman, and sarin fragments generated by 20S proteasomes in different conditions, [100,101]. Repeatedly, a large number of studies have suggested an such as the in presence of constitutive or immunoproteasome and association between major cardiovascular diseases and the 192 R the activator PA28-(cid:1)(cid:2). Thus, in a systems biology framework, the variant. Moreover, the results of a recent meta-analysis [102] show tool ProteaMAlg could offer a quantitative scheme to study the that PON1 gene variants at codon 192 impact on the probability of degradation dynamics in different experimental conditions defined attaining longevity, and that subjects carrying RR and QR by the substrate specificity and the type of proteasome. Other genotypes (R+ carriers) are favoured in reaching extreme ages. important aspect of proteasome degradation mechanisms, such as These results likely represent the counterpart of the effects observed the peptide translocation, which can have a great impact on the on CVD, as centenarians and nonagenarians escaped or delayed the quality of epitope production, has been recently modeled [21,80]. onset of the major age-related diseases, including CVD. Lastly, another issue to be addressed in a systems biology The role of PON1 emerges out of this brief picture: it can be perspective is the identification and characterization of molecules located at the centre of a complex interaction of physiological that could modulate (enhancing or inhibiting) the proteasome processes, impacting on many human complex disease, and thus the activity leading to the development of proteasome-based drugs able best starting point for a systems biology analysis. to counteract aging and age-related pathologies. Indeed, at present, To this regard, we reconstructed two PON1 networks, using the only available proteasome-based therapeutic approach is the Cytoscape and Agilent literature search plugin. This plugin takes as administration of proteasome inhibitors such as Bortezomib, in the input a set of genes of interest, extracts interactions and generates treatment of different cancers [81] while new compounds have putative networks from literature. recently shown to be able to discriminate between these two main The first network shown in Fig. (5) is built using only PON-1 as isoforms of 20S proteasome, constitutive and immunoproteasome [82]. The identification of natural/synthetic molecules able to input, and displays mainly the proximal nodes that have a direct enhance proteasome activity (and maybe with higher affinity for connection with it. different proteasome subtypes) would allow to target other type of As expected, PON-1 is directly connected to APO-E, but it is diseases where proteasome activity should be enhanced and not worth noting that PON-1 is also directly connected to SP1 and inhibited. For example, oleuropein, the most abundant of the PPARG (two broadly acting transcription factors which are phenolic compounds in Olea europaea leaf extract, olive oil, and sensitive to oxidative stress), to PSMC6 (a subunit of the protea- olives has a stimulatory impact on proteasome activities in vitro, some), to HSD11B1 (a gene encoding a microsomal enzyme that Systems Biology and Longevity Current Pharmaceutical Design, 2010, Vol. 16, No. 7 809 Fig. (5). Basic PON-1 literature search-based network. This network shows PON-1 first degree interactions, highlighting important connections between PON-1 and APO-E, SP1, PPARG, PSMC6, HSD11B1 and SREBF-2. Fig. (6). Extended PON-1 literature search-based network. This expanded network shows PON-1 long-range interactions indicating how PON-1 can affect a number of different physiological processes. catalyzes the reversible conversion of the stress hormone cortisol to In this network is possible to see long range influences of PON1 in the inactive metabolite cortisone) and to SREBF-2 (a transcription different physiological aspects. factor that controls cholesterol homeostasis). All these genes are The presence of an inflammatory cluster can be noted, involved in oxidative stress, cardiovascular diseases and many other including a master regulator of inflammation, NF-(cid:1)B, and different age-associated pathologies. inflammatory mediators, such as IL-1 beta and IL-15. In addition, The second network shown in Fig. (6) is built using the PON1 the presence of a cluster regarding the production of lipophylic interacting genes of the first network as the input set and it displays hormones and modulators of inflammation can be noted. Indeed, the connections among the PON1 interacting genes and other genes. this cluster contains enzymes that catalyzes the production of 810 Current Pharmaceutical Design, 2010, Vol. 16, No. 7 Cevenini et al. prostaglandins (AKR1C3) or steroids (HSD11B1, SRD5A2, a systems biology approach, in a recursive process of “wet” and CYP11A1, CYP21A2, HSD3B2, HSD17B3, CYP19A1). “dry” experiments, as represented in Fig. (7). The central cluster is related to the control of cell cycle and Indeed, the existing information and new data generated by ad proliferation and it is connected to a cluster of epithelial hoc experiments are put together in a comprehensive data set, differentiation related genes by epidermal growth factor (EGF). which is then analysed and used to generate predictive mathema- This cluster is also connected to a cluster of vascular remodelling tical models. These new models can suggest new experiments, related genes (VEGF) by some oxidative and stress response related which in turn produce an enlarged data set and the analytic process genes such as ABL2, underlining the connection between oxidative restarts. This innovative approach can be performed only if specific stress response and vascular remodelling. expertise of analysis and computational power will be available, joint to more collaboration across disciplines and countries. We 4. CONCLUSIONS surmise that this integrated approach will let us understand the In this complex scenario we briefly summarized, it is emerging aging process at organism level and to identify targets for anti- that systems biology is a powerful tool to integrate and quantify aging interventions. high dimensionality but still fragmented data that are produced by increasing high-throughput technologies in the field of genomics, ACKNOWLEDGEMENTS proteomics, metabolomics and other –omics. In particular, our This work was supported by: the EU Projects "Proteomage" laboratory is producing these data in the framework of several (FP6-518230) and "MARK-AGE" (FP7-200880) to CF; Roberto integrated projects on human aging and longevity financed by and Cornelia Pallotti Legacy for Cancer Research Grants to CF and European Commission. Some are already concluded, such as SS; University of Bologna “Progetti Strategici” 2006 grant (“p53 e ECHA, T-CIA, FUNCTIONAGE, PROTAGE, and others are still patologie non neoplastiche nell'anziano: uno studio multidis- in progress, including GEHA, PROTEOMAGE, MARK-AGE, ciplinare sul ruolo del polimorfismo al codone 72 del gene TP53”) MYOAGE, RISTOMED and WhyWeAge. We propose that a to SS. AZ acknowledges the UCLH/UCL NIHR Comprehensive comprehensive analysis of this information is possible only through Biomedical Research Centre and VW foundation. Fig. (7). The “dry”/"wet” recursive spiral of the systems biology of aging and its implementation. 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